iEMSs 2016

1. Developing a stochastic simulation model for the generation
of residential water end-use demand time series
A. Cominola1, M. Giuliani1, A. Castelletti1, A.M. Abdallah2, D.E. Rosenberg2
1 Dept. Electronics, Information, and Bioengineering - Hydroinformatics Lab, Politecnico di Milano
2 Dept. of Civil and Environmental Engineering, Utah State University

2. NRM
Urban population is growing
US
246.2
Urban population in millions
81%
Urban percentage
Mexico
84.392
77%
Colombia
34.3
73%
Brazil
162.6
85%
Argentina
35.6
90%
Ukraine
30.9
68%
Russia
103.6
73%
China
559.2Urban population in millions
42%Urban percentage
Turkey
51.1
68%
India
329.3
29%
Bangladesh
38.2
26%
Philippines
55.0
64%
Indonesia
114.1
50%
S Korea
39.0
81%
Japan
84.7
66%
Egypt
33.1
43%
S Africa
28.6
60%
Canada
26.3
Venezuela
26.0
Poland
23.9
Thailand
21.5
Australia
18.3
Netherlands
13.3
Peru
21.0
Saudi Arabia
20.9
Iraq
20.3
Vietnam
23.3
DR Congo
20.2
Algeria
22.0Morocco
19.4
Malaysia
18.1
Burma
16.5
Sudan
16.3
Chile
14.6
N Korea
14.1
Ethiopia
13.0
Uzbekistan
10.1
Tanzania
9.9
Romania
11.6
Ghana
11.3
Syria
10.2
Belgium
10.2
80%
94%
62%
33%
89%
81%
73%
81%
67%
27%
33%
65%
60%
69%
32%
43%
88%
62%
16%
37%
25%
54%
49%
51%
97%
Nigeria
68.6
50%
UK
54.0
90%
France
46.9
77%
Spain
33.6
77%
Italy
39.6
68%
Germany
62.0
75%
Iran
48.4
68%
Pakistan
59.3
36%
Cameroon
Angola
Ecuador
Ivory
Coast
Kazakh-
stan
Cuba
Afghan-
istan
Sweden
Kenya
Czech
Republic
9.5
9.3
8.7
8.6
8.6
8.5
7.8
7.6
7.6
7.4
Mozam-
bique
Hong
Kong
Belarus
Tunisia
Hungary
Greece
Israel
Guate-
mala
Portugal
Yemen
Dominican
Republic
Bolivia
Serbia &
Mont
Switzer-
land
Austria
Bulgaria
Mada-
gascar
Libya
Senegal
Jordan
Zimbabwe
Nepal
Denmark
Mali
Azerbaijan
Singapore
El
Salvador
Zambia
Uganda
Puerto
Rico
Paraguay
UAE
Benin
Norway
New
Zealand
Honduras
Haiti
Nicaragua
Guinea
Finland
Uruguay
Lebanon
Somalia
Sri Lanka
Cambodia
Slovakia
Costa Rica
Palestine
Kuwait
Togo
Chad
Burkina
Ireland
Croatia
Congo
Niger
Sierra Leone
Malawi
Panama
Turkmenistan
Georgia
Lithuania
Liberia
Moldova
Rwanda
Kyrgyzstan
Oman
Armenia
Bosnia
Tajikistan
CAR
Melanesia
Latvia
Mongolia
Albania
Jamaica
Macedonia
Mauritania Laos
Gabon
Botswana
Slovenia
Eritrea
Estonia
Gambia
Burundi
Papua New Guinea
Namibia
Mauritius
Guinea-Bissau
Lesotho E Timor
Bhutan
Swaziland
Trinidad & Tobago
The earth reaches a momentous
milestone: by next year, for the first time
in history, more than half its population
will be living in cities. Those 3.3 billion
people are expected to grow to 5 billion
by 2030 — this unique map of the world
shows where those people live now
At the beginning of the 20th
century, the world's urban
population was only 220
million, mainly in the west
By 2030, the towns and
cities of the developing
world will make up 80%
of urban humanity
The new urban world
Urban growth, 2005—2010
Predominantly urban
75% or over
Predominantly urban
50—74%
Predominantly rural
25—49% urban
Predominantly rural
0—24% urban
Cities over 10 million people
(greater urban area)
Key
Tokyo
33.4
Osaka
16.6
Seoul
23.2
Manila
15.4
Jakarta
14.9
Dacca
13.8
Bombay
21.3
Delhi
21.1 Calcutta
15.5
Karachi
14.8
Shanghai
17.3
Canton
14.5
Beijing
12.7
Moscow
13.4
Tehran
12.1
Cairo
15.9
Istanbul
11.7
London
12.0
Lagos
10.0
Mexico
City
22.1
New York
21.8
Sao Paulo
20.4
LA
17.9
Rio de
Janeiro
12.2
Buenos
Aires
13.5
3,307,950,000The world’s urban population — from a total of 6,615.9 million SOURCE: UNFPA GRAPHIC: PAUL SCRUTONAfrica Asia Oceania Europe
0.1%
Eastern Europe
-0.4%
Arab States
Latin America
& Caribbean North America
3.2%
2.4%
1.3%
2.8%
1.7%
1.3%

3. NRM
Residential water demand management
resolution depends on the installed meter, the logging time can be
shortened without installation of smart meters but simply
increasing the traditional reading frequency by the users. However,
so far only ad-hoc studies systematically collected and analyzed
data at daily resolution (e.g., Olmstead et al., 2007; Wong et al.,
2010) and few water companies (e.g., Water Corporation in West-
ern Australia and Thames Water in London) started increasing their
reading frequency by direct involvement of their customers, who
reconstructing the average flow within the pipe with a resolu-
tion of 0.015 L (Kim et al., 2008).
Ultrasonic sensors (Mori et al., 2004), which estimate the flow
velocity, and then determine the flow rate knowing the pipe
section, by measuring the difference in time between ultrasonic
beams generated by piezoelectric devices and transmitted
within the water flow. The transducers are generally operated in
the range 0.5e2 MHz and allow attaining an average resolution
around 0.0018 L (e.g., Sanderson and Yeung, 2002).
Pressure sensors (Froehlich et al., 2009, 2011), which consist in
steel devices, equipped with an analog-digital converter and a
micro-controller, continuously sampling pressure with a theo-
retical maximum resolution of 2 kHZ. Flow rate is related to the
pressure change generated by the opening/close of the water
devices valves via Poiseuille's Law.
Flow meters (Mayer and DeOreo, 1999), which exploit the water
flow to spin either pistons (mechanic flow meters) or magnets
(magnetic meters) and correlate the number of revolutions or
pulse to the water volume passing through the pipe. Sensing
resolution spans between 34.2 and 72 pulses per liter (i.e., 1
pulse every 0.029 and 0.014 L, respectively) associated to a
logging frequency in the range of 1e10 s (Kowalski and
Marshallsay, 2005; Heinrich, 2007; Willis et al., 2013).
So far, only flow meters and pressure sensors have been
employed in smart meters applications because ultrasonic sensors
are too costly and the use of accelerometers requires an intrusive
calibration phase with the placement of multiple meters distrib-
uted on the pipe network for each single device of interest (Kim
et al., 2008). It is worth noting that the “smartness” of these sen-
Fig. 2. Five-years count of the 134 publications reviewed in this study.
A. Cominola et al. / Environmental Modelling Software 72 (2015) 198e214200
Benefits and challenges of using smart meters for advancing
residential water demand modeling and management: A review
A. Cominola a
, M. Giuliani a
, D. Piga b
, A. Castelletti a, c, *
, A.E. Rizzoli d
a
Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy
b
IMT Institute for Advanced Studies Lucca, Lucca, Italy
c
Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland
d
Istituto Dalle Molle di Studi sull'Intelligenza Artificiale, SUPSI-USI, Lugano, Switzerland
a r t i c l e i n f o
Article history:
Received 2 April 2015
Received in revised form
21 July 2015
Accepted 21 July 2015
Available online xxx
Keywords:
Smart meter
Residential water management
Water demand modeling
Water conservation
a b s t r a c t
Over the last two decades, water smart metering programs have been launched in a number of medium
to large cities worldwide to nearly continuously monitor water consumption at the single household
level. The availability of data at such very high spatial and temporal resolution advanced the ability in
characterizing, modeling, and, ultimately, designing user-oriented residential water demand manage-
ment strategies. Research to date has been focusing on one or more of these aspects but with limited
integration between the specialized methodologies developed so far. This manuscript is the first
comprehensive review of the literature in this quickly evolving water research domain. The paper
contributes a general framework for the classification of residential water demand modeling studies,
which allows revising consolidated approaches, describing emerging trends, and identifying potential
future developments. In particular, the future challenges posed by growing population demands, con-
strained sources of water supply and climate change impacts are expected to require more and more
integrated procedures for effectively supporting residential water demand modeling and management in
several countries across the world.
© 2015 Elsevier Ltd. All rights reserved.
1. Introduction
World's urban population is expected to raise from current
54%e66% in 2050 and to further increase as a consequence of the
unlikely stabilization of human population by the end of the cen-
tury (Gerland et al., 2014). By 2030 the number of mega-cities,
namely cities with more than 10 million inhabitants, will grow
over 40 (UNDESA, 2010). This will boost residential water demand
(Cosgrove and Cosgrove, 2012), which nowadays covers a large
portion of the public drinking water supply worldwide (e.g.,
60e80% in Europe (Collins et al., 2009), 58% in the United States
(Kenny et al., 2009)).
The concentration of the water demands of thousands or mil-
lions of people into small areas will considerably raise the stress on
finite supplies of available freshwater (McDonald et al., 2011a).
Besides, climate and land use change will further increase the
number of people facing water shortage (McDonald et al., 2011b). In
such context, water supply expansion through the construction of
new infrastructures might be an option to escape water stress in
some situations. Yet, geographical or financial limitations largely
restrict such options in most countries (McDonald et al., 2014).
Here, acting on the water demand management side through the
promotion of cost-effective water-saving technologies, revised
economic policies, appropriate national and local regulations, and
education represents an alternative strategy for securing reliable
water supply and reduce water utilities' costs (Gleick et al., 2003).
In recent years, a variety of water demand management stra-
tegies (WDMS) has been applied (for a review, see Inman and
Jeffrey, 2006, and references therein). However, the effectiveness
of these WDMS is often context-specific and strongly depends on
our understanding of the drivers inducing people to consume or
save water (Jorgensen et al., 2009). Models that quantitatively
describe how water demand is influenced and varies in relation to
exogenous uncontrolled drivers (e.g., seasonality, climatic condi-
tions) and demand management actions (e.g., water restrictions,
pricing schemes, education campaigns) are essential to explore
water users' response to alternative WDMS, ultimately supporting
* Corresponding author. Department of Electronics, Information, and Bioengi-
neering, Politecnico di Milano, Milan, Italy.
E-mail address: andrea.castelletti@polimi.it (A. Castelletti).
Contents lists available at ScienceDirect
Environmental Modelling Software
journal homepage: www.elsevier.com/locate/envsoft
http://dx.doi.org/10.1016/j.envsoft.2015.07.012
Environmental Modelling Software 72 (2015) 198e214
Benefits and challenges of using smart meters for advancing
residential water demand modeling and management: A review
A. Cominola a
, M. Giuliani a
, D. Piga b
, A. Castelletti a, c, *
, A.E. Rizzoli d
a
Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy
b
IMT Institute for Advanced Studies Lucca, Lucca, Italy
c
Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland
d
Istituto Dalle Molle di Studi sull'Intelligenza Artificiale, SUPSI-USI, Lugano, Switzerland
a r t i c l e i n f o
Article history:
Received 2 April 2015
Received in revised form
21 July 2015
Accepted 21 July 2015
Available online xxx
Keywords:
Smart meter
Residential water management
Water demand modeling
Water conservation
a b s t r a c t
Over the last two decades, water smart metering programs have been launched in a numbe
to large cities worldwide to nearly continuously monitor water consumption at the singl
level. The availability of data at such very high spatial and temporal resolution advanced t
characterizing, modeling, and, ultimately, designing user-oriented residential water dema
ment strategies. Research to date has been focusing on one or more of these aspects but
integration between the specialized methodologies developed so far. This manuscript
comprehensive review of the literature in this quickly evolving water research domain
contributes a general framework for the classification of residential water demand mode
which allows revising consolidated approaches, describing emerging trends, and identifyi
future developments. In particular, the future challenges posed by growing population de
strained sources of water supply and climate change impacts are expected to require mo
integrated procedures for effectively supporting residential water demand modeling and ma
several countries across the world.
© 2015 Elsevier Ltd. All righ
1. Introduction
World's urban population is expected to raise from current
54%e66% in 2050 and to further increase as a consequence of the
unlikely stabilization of human population by the end of the cen-
tury (Gerland et al., 2014). By 2030 the number of mega-cities,
namely cities with more than 10 million inhabitants, will grow
over 40 (UNDESA, 2010). This will boost residential water demand
(Cosgrove and Cosgrove, 2012), which nowadays covers a large
portion of the public drinking water supply worldwide (e.g.,
60e80% in Europe (Collins et al., 2009), 58% in the United States
(Kenny et al., 2009)).
The concentration of the water demands of thousands or mil-
lions of people into small areas will considerably raise the stress on
finite supplies of available freshwater (McDonald et al., 2011a).
Besides, climate and land use change will further increase the
number of people facing water shortage (McDonald et al
such context, water supply expansion through the con
new infrastructures might be an option to escape wat
some situations. Yet, geographical or financial limitati
restrict such options in most countries (McDonald et
Here, acting on the water demand management side t
promotion of cost-effective water-saving technologi
economic policies, appropriate national and local regul
education represents an alternative strategy for secur
water supply and reduce water utilities' costs (Gleick e
In recent years, a variety of water demand manage
tegies (WDMS) has been applied (for a review, see
Jeffrey, 2006, and references therein). However, the ef
of these WDMS is often context-specific and strongly d
our understanding of the drivers inducing people to c
save water (Jorgensen et al., 2009). Models that qu
describe how water demand is influenced and varies in
exogenous uncontrolled drivers (e.g., seasonality, clim
tions) and demand management actions (e.g., water r
pricing schemes, education campaigns) are essential
water users' response to alternative WDMS, ultimately
* Corresponding author. Department of Electronics, Information, and Bioengi-
neering, Politecnico di Milano, Milan, Italy.
E-mail address: andrea.castelletti@polimi.it (A. Castelletti).
Contents lists available at ScienceDirect
Environmental Modelling Software
journal homepage: www.elsevier.com/locate/envsoft
http://dx.doi.org/10.1016/j.envsoft.2015.07.012
1364-8152/© 2015 Elsevier Ltd. All rights reserved.
Environmental Modelling Software 72 (2015) 198e214
36%
43%
13%
6%
1%
Analysis of 134 studies over the last 25 years

4. NRM
Residential water demand management
resolution depends on the installed meter, the logging time can be
shortened without installation of smart meters but simply
increasing the traditional reading frequency by the users. However,
so far only ad-hoc studies systematically collected and analyzed
data at daily resolution (e.g., Olmstead et al., 2007; Wong et al.,
2010) and few water companies (e.g., Water Corporation in West-
ern Australia and Thames Water in London) started increasing their
reading frequency by direct involvement of their customers, who
reconstructing the average flow within the pipe with a resolu-
tion of 0.015 L (Kim et al., 2008).
Ultrasonic sensors (Mori et al., 2004), which estimate the flow
velocity, and then determine the flow rate knowing the pipe
section, by measuring the difference in time between ultrasonic
beams generated by piezoelectric devices and transmitted
within the water flow. The transducers are generally operated in
the range 0.5e2 MHz and allow attaining an average resolution
around 0.0018 L (e.g., Sanderson and Yeung, 2002).
Pressure sensors (Froehlich et al., 2009, 2011), which consist in
steel devices, equipped with an analog-digital converter and a
micro-controller, continuously sampling pressure with a theo-
retical maximum resolution of 2 kHZ. Flow rate is related to the
pressure change generated by the opening/close of the water
devices valves via Poiseuille's Law.
Flow meters (Mayer and DeOreo, 1999), which exploit the water
flow to spin either pistons (mechanic flow meters) or magnets
(magnetic meters) and correlate the number of revolutions or
pulse to the water volume passing through the pipe. Sensing
resolution spans between 34.2 and 72 pulses per liter (i.e., 1
pulse every 0.029 and 0.014 L, respectively) associated to a
logging frequency in the range of 1e10 s (Kowalski and
Marshallsay, 2005; Heinrich, 2007; Willis et al., 2013).
So far, only flow meters and pressure sensors have been
employed in smart meters applications because ultrasonic sensors
are too costly and the use of accelerometers requires an intrusive
calibration phase with the placement of multiple meters distrib-
uted on the pipe network for each single device of interest (Kim
et al., 2008). It is worth noting that the “smartness” of these sen-
Fig. 2. Five-years count of the 134 publications reviewed in this study.
A. Cominola et al. / Environmental Modelling Software 72 (2015) 198e214200
Benefits and challenges of using smart meters for advancing
residential water demand modeling and management: A review
A. Cominola a
, M. Giuliani a
, D. Piga b
, A. Castelletti a, c, *
, A.E. Rizzoli d
a
Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy
b
IMT Institute for Advanced Studies Lucca, Lucca, Italy
c
Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland
d
Istituto Dalle Molle di Studi sull'Intelligenza Artificiale, SUPSI-USI, Lugano, Switzerland
a r t i c l e i n f o
Article history:
Received 2 April 2015
Received in revised form
21 July 2015
Accepted 21 July 2015
Available online xxx
Keywords:
Smart meter
Residential water management
Water demand modeling
Water conservation
a b s t r a c t
Over the last two decades, water smart metering programs have been launched in a number of medium
to large cities worldwide to nearly continuously monitor water consumption at the single household
level. The availability of data at such very high spatial and temporal resolution advanced the ability in
characterizing, modeling, and, ultimately, designing user-oriented residential water demand manage-
ment strategies. Research to date has been focusing on one or more of these aspects but with limited
integration between the specialized methodologies developed so far. This manuscript is the first
comprehensive review of the literature in this quickly evolving water research domain. The paper
contributes a general framework for the classification of residential water demand modeling studies,
which allows revising consolidated approaches, describing emerging trends, and identifying potential
future developments. In particular, the future challenges posed by growing population demands, con-
strained sources of water supply and climate change impacts are expected to require more and more
integrated procedures for effectively supporting residential water demand modeling and management in
several countries across the world.
© 2015 Elsevier Ltd. All rights reserved.
1. Introduction
World's urban population is expected to raise from current
54%e66% in 2050 and to further increase as a consequence of the
unlikely stabilization of human population by the end of the cen-
tury (Gerland et al., 2014). By 2030 the number of mega-cities,
namely cities with more than 10 million inhabitants, will grow
over 40 (UNDESA, 2010). This will boost residential water demand
(Cosgrove and Cosgrove, 2012), which nowadays covers a large
portion of the public drinking water supply worldwide (e.g.,
60e80% in Europe (Collins et al., 2009), 58% in the United States
(Kenny et al., 2009)).
The concentration of the water demands of thousands or mil-
lions of people into small areas will considerably raise the stress on
finite supplies of available freshwater (McDonald et al., 2011a).
Besides, climate and land use change will further increase the
number of people facing water shortage (McDonald et al., 2011b). In
such context, water supply expansion through the construction of
new infrastructures might be an option to escape water stress in
some situations. Yet, geographical or financial limitations largely
restrict such options in most countries (McDonald et al., 2014).
Here, acting on the water demand management side through the
promotion of cost-effective water-saving technologies, revised
economic policies, appropriate national and local regulations, and
education represents an alternative strategy for securing reliable
water supply and reduce water utilities' costs (Gleick et al., 2003).
In recent years, a variety of water demand management stra-
tegies (WDMS) has been applied (for a review, see Inman and
Jeffrey, 2006, and references therein). However, the effectiveness
of these WDMS is often context-specific and strongly depends on
our understanding of the drivers inducing people to consume or
save water (Jorgensen et al., 2009). Models that quantitatively
describe how water demand is influenced and varies in relation to
exogenous uncontrolled drivers (e.g., seasonality, climatic condi-
tions) and demand management actions (e.g., water restrictions,
pricing schemes, education campaigns) are essential to explore
water users' response to alternative WDMS, ultimately supporting
* Corresponding author. Department of Electronics, Information, and Bioengi-
neering, Politecnico di Milano, Milan, Italy.
E-mail address: andrea.castelletti@polimi.it (A. Castelletti).
Contents lists available at ScienceDirect
Environmental Modelling Software
journal homepage: www.elsevier.com/locate/envsoft
http://dx.doi.org/10.1016/j.envsoft.2015.07.012
Environmental Modelling Software 72 (2015) 198e214
Benefits and challenges of using smart meters for advancing
residential water demand modeling and management: A review
A. Cominola a
, M. Giuliani a
, D. Piga b
, A. Castelletti a, c, *
, A.E. Rizzoli d
a
Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy
b
IMT Institute for Advanced Studies Lucca, Lucca, Italy
c
Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland
d
Istituto Dalle Molle di Studi sull'Intelligenza Artificiale, SUPSI-USI, Lugano, Switzerland
a r t i c l e i n f o
Article history:
Received 2 April 2015
Received in revised form
21 July 2015
Accepted 21 July 2015
Available online xxx
Keywords:
Smart meter
Residential water management
Water demand modeling
Water conservation
a b s t r a c t
Over the last two decades, water smart metering programs have been launched in a numbe
to large cities worldwide to nearly continuously monitor water consumption at the singl
level. The availability of data at such very high spatial and temporal resolution advanced t
characterizing, modeling, and, ultimately, designing user-oriented residential water dema
ment strategies. Research to date has been focusing on one or more of these aspects but
integration between the specialized methodologies developed so far. This manuscript
comprehensive review of the literature in this quickly evolving water research domain
contributes a general framework for the classification of residential water demand mode
which allows revising consolidated approaches, describing emerging trends, and identifyi
future developments. In particular, the future challenges posed by growing population de
strained sources of water supply and climate change impacts are expected to require mo
integrated procedures for effectively supporting residential water demand modeling and ma
several countries across the world.
© 2015 Elsevier Ltd. All righ
1. Introduction
World's urban population is expected to raise from current
54%e66% in 2050 and to further increase as a consequence of the
unlikely stabilization of human population by the end of the cen-
tury (Gerland et al., 2014). By 2030 the number of mega-cities,
namely cities with more than 10 million inhabitants, will grow
over 40 (UNDESA, 2010). This will boost residential water demand
(Cosgrove and Cosgrove, 2012), which nowadays covers a large
portion of the public drinking water supply worldwide (e.g.,
60e80% in Europe (Collins et al., 2009), 58% in the United States
(Kenny et al., 2009)).
The concentration of the water demands of thousands or mil-
lions of people into small areas will considerably raise the stress on
finite supplies of available freshwater (McDonald et al., 2011a).
Besides, climate and land use change will further increase the
number of people facing water shortage (McDonald et al
such context, water supply expansion through the con
new infrastructures might be an option to escape wat
some situations. Yet, geographical or financial limitati
restrict such options in most countries (McDonald et
Here, acting on the water demand management side t
promotion of cost-effective water-saving technologi
economic policies, appropriate national and local regul
education represents an alternative strategy for secur
water supply and reduce water utilities' costs (Gleick e
In recent years, a variety of water demand manage
tegies (WDMS) has been applied (for a review, see
Jeffrey, 2006, and references therein). However, the ef
of these WDMS is often context-specific and strongly d
our understanding of the drivers inducing people to c
save water (Jorgensen et al., 2009). Models that qu
describe how water demand is influenced and varies in
exogenous uncontrolled drivers (e.g., seasonality, clim
tions) and demand management actions (e.g., water r
pricing schemes, education campaigns) are essential
water users' response to alternative WDMS, ultimately
* Corresponding author. Department of Electronics, Information, and Bioengi-
neering, Politecnico di Milano, Milan, Italy.
E-mail address: andrea.castelletti@polimi.it (A. Castelletti).
Contents lists available at ScienceDirect
Environmental Modelling Software
journal homepage: www.elsevier.com/locate/envsoft
http://dx.doi.org/10.1016/j.envsoft.2015.07.012
1364-8152/© 2015 Elsevier Ltd. All rights reserved.
Environmental Modelling Software 72 (2015) 198e214
36%
43%
13%
6%
1%
Analysis of 134 studies over the last 25 years
first smart
meters
deployment

5. NRM
Traditional water meters
monthly to yearly readings
Oct Nov Dec
Waterconsumptionm3
?
1 cubic meter

6. NRM
Oct Nov Dec
Waterconsumptionm3
?
Smart water meters
Data logging at 5-10 sec72 pulses/L

7. Smart meters deployment: OPPORTUNITIES
• End-use characterization
• Advanced management strategies
• Projections of water demand

8. Smart meters deployment: CHALLENGES
• Deployment and maintenance costs
• Big data collection, processing and analysis
• Intrusiveness and privacy

9. NRM
Generation of end-use water demand
5 Devices (indoor): toilet, clothes washer, showers, dishwasher, faucet
753,076 water-use events
over 4,036 monitoring days
across 9 US cities
Source: DeOreo [2011]

10. NRM
Generation of end-use water demand
DEVELOPMENT OF A SYNTHETIC WATER END-USE PATTERNS GENERATOR
An algorithm to generate synthetic water end-use patterns has been developed within the SmartH2O project, with the double pu
• building end-use water consumption datasets to feed disaggregation algorithms and to provide benchmark datasets for com
testing;
• allowing for the generation of end-use patterns under different demographic and technological scenarios.
DEVELOPMENT PLAN
• User-friendly interface
• Web portal to contribute with new
datasets from different case studies
CURRENT FEATURES
• Trained on high-resolutions (1 second) consumption data from 9 cities across USA
• Performance validated with a two-sample Kolmogorov-Smirnov test
• Flexible for synthetic generation at multi-scale resolutions.
user’s input end-use statistics extraction end-use traces generation
• house size
• time sampling
resolution
• device
presence
usage duration
usage volume
time-of-use
frequency of use
typical pattern (signature)
34 %
Andrea Cominol
andrea.cominola@polimi
@smartH2Oproje
#SmartH2
time
water
consumption
5 Devices (indoor): toilet, clothes washer, showers, dishwasher, faucet
753,076 water-use events
over 4,036 monitoring days
across 9 US cities
Source: DeOreo [2011]

11. NRM
Generation of end-use water demand
DEVELOPMENT OF A SYNTHETIC WATER END-USE PATTERNS GENERATOR
An algorithm to generate synthetic water end-use patterns has been developed within the SmartH2O project, with the double pu
• building end-use water consumption datasets to feed disaggregation algorithms and to provide benchmark datasets for com
testing;
• allowing for the generation of end-use patterns under different demographic and technological scenarios.
DEVELOPMENT PLAN
• User-friendly interface
• Web portal to contribute with new
datasets from different case studies
CURRENT FEATURES
• Trained on high-resolutions (1 second) consumption data from 9 cities across USA
• Performance validated with a two-sample Kolmogorov-Smirnov test
• Flexible for synthetic generation at multi-scale resolutions.
user’s input end-use statistics extraction end-use traces generation
• house size
• time sampling
resolution
• device
presence
usage duration
usage volume
time-of-use
frequency of use
typical pattern (signature)
34 %
Andrea Cominol
andrea.cominola@polimi
@smartH2Oproje
#SmartH2
time
water
consumption
5 Devices (indoor): toilet, clothes washer, showers, dishwasher, faucet
Duration
Volume
Time of use
# daily uses

12. NRM
Generation of end-use water demand
DEVELOPMENT OF A SYNTHETIC WATER END-USE PATTERNS GENERATOR
An algorithm to generate synthetic water end-use patterns has been developed within the SmartH2O project, with the double pu
• building end-use water consumption datasets to feed disaggregation algorithms and to provide benchmark datasets for com
testing;
• allowing for the generation of end-use patterns under different demographic and technological scenarios.
DEVELOPMENT PLAN
• User-friendly interface
• Web portal to contribute with new
datasets from different case studies
CURRENT FEATURES
• Trained on high-resolutions (1 second) consumption data from 9 cities across USA
• Performance validated with a two-sample Kolmogorov-Smirnov test
• Flexible for synthetic generation at multi-scale resolutions.
user’s input end-use statistics extraction end-use traces generation
• house size
• time sampling
resolution
• device
presence
usage duration
usage volume
time-of-use
frequency of use
typical pattern (signature)
34 %
Andrea Cominol
andrea.cominola@polimi
@smartH2Oproje
#SmartH2
time
water
consumption
5 Devices (indoor): toilet, clothes washer, showers, dishwasher, faucet
Duration
Volume
Time of use
# daily uses
Typical pattern -
signature

13. NRM
Generation of end-use water demand
DEVELOPMENT OF A SYNTHETIC WATER END-USE PATTERNS GENERATOR
An algorithm to generate synthetic water end-use patterns has been developed within the SmartH2O project, with the double pu
• building end-use water consumption datasets to feed disaggregation algorithms and to provide benchmark datasets for com
testing;
• allowing for the generation of end-use patterns under different demographic and technological scenarios.
DEVELOPMENT PLAN
• User-friendly interface
• Web portal to contribute with new
datasets from different case studies
CURRENT FEATURES
• Trained on high-resolutions (1 second) consumption data from 9 cities across USA
• Performance validated with a two-sample Kolmogorov-Smirnov test
• Flexible for synthetic generation at multi-scale resolutions.
user’s input end-use statistics extraction end-use traces generation
• house size
• time sampling
resolution
• device
presence
usage duration
usage volume
time-of-use
frequency of use
typical pattern (signature)
34 %
Andrea Cominol
andrea.cominola@polimi
@smartH2Oproje
#SmartH2
time
water
consumption
5 Devices (indoor): toilet, clothes washer, showers, dishwasher, faucet

14. NRM
Generation of end-use water demand
DEVELOPMENT OF A SYNTHETIC WATER END-USE PATTERNS GENERATOR
An algorithm to generate synthetic water end-use patterns has been developed within the SmartH2O project, with the double pu
• building end-use water consumption datasets to feed disaggregation algorithms and to provide benchmark datasets for com
testing;
• allowing for the generation of end-use patterns under different demographic and technological scenarios.
DEVELOPMENT PLAN
• User-friendly interface
• Web portal to contribute with new
datasets from different case studies
CURRENT FEATURES
• Trained on high-resolutions (1 second) consumption data from 9 cities across USA
• Performance validated with a two-sample Kolmogorov-Smirnov test
• Flexible for synthetic generation at multi-scale resolutions.
user’s input end-use statistics extraction end-use traces generation
• house size
• time sampling
resolution
• device
presence
usage duration
usage volume
time-of-use
frequency of use
typical pattern (signature)
34 %
Andrea Cominol
andrea.cominola@polimi
@smartH2Oproje
#SmartH2
time
water
consumption
5 Devices (indoor): toilet, clothes washer, showers, dishwasher, faucet

15. NRM
Generation of end-use water demand
DEVELOPMENT OF A SYNTHETIC WATER END-USE PATTERNS GENERATOR
An algorithm to generate synthetic water end-use patterns has been developed within the SmartH2O project, with the double pu
• building end-use water consumption datasets to feed disaggregation algorithms and to provide benchmark datasets for com
testing;
• allowing for the generation of end-use patterns under different demographic and technological scenarios.
DEVELOPMENT PLAN
• User-friendly interface
• Web portal to contribute with new
datasets from different case studies
CURRENT FEATURES
• Trained on high-resolutions (1 second) consumption data from 9 cities across USA
• Performance validated with a two-sample Kolmogorov-Smirnov test
• Flexible for synthetic generation at multi-scale resolutions.
user’s input end-use statistics extraction end-use traces generation
• house size
• time sampling
resolution
• device
presence
usage duration
usage volume
time-of-use
frequency of use
typical pattern (signature)
34 %
Andrea Cominol
andrea.cominola@polimi
@smartH2Oproje
#SmartH2
time
water
consumption
5 Devices: toilet, clothes washer, showers, dishwasher, faucet

16. Potential applications

17. NRM
End use characterization

18. NRM
Advanced management strategies

19. NRM
Projections of water demand: # inhabitants

20. NRM
Projections of water demand: device efficiency
- 40%
- 50%
- 2%

21. NRM
Conclusions
This preliminary results show great potential for supporting smart meters
deployments and end-use water demand analysis.
Outlook:
1. Update of the database with new report by Aquacraft Inc.
2. Web service for community development
3. Creation of open database across countries

22. thank you
Matteo Giuliani
matteo.giuliani@polimi.it
http://giuliani.faculty.polimi.it